scholarly journals Development of an openEHR Template for COVID-19 Based on Clinical Guidelines

10.2196/20239 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e20239 ◽  
Author(s):  
Mengyang Li ◽  
Heather Leslie ◽  
Bin Qi ◽  
Shan Nan ◽  
Hongshuo Feng ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Model ◽  
Data Exchange ◽  
Disease Diagnosis ◽  
Information Modeling ◽  
Diagnosis And Treatment ◽  
Data Repository ◽  
Test Case ◽  
Modeling Framework ◽  
Shared Data

Background The coronavirus disease (COVID-19) was discovered in China in December 2019. It has developed into a threatening international public health emergency. With the exception of China, the number of cases continues to increase worldwide. A number of studies about disease diagnosis and treatment have been carried out, and many clinically proven effective results have been achieved. Although information technology can improve the transferring of such knowledge to clinical practice rapidly, data interoperability is still a challenge due to the heterogeneous nature of hospital information systems. This issue becomes even more serious if the knowledge for diagnosis and treatment is updated rapidly as is the case for COVID-19. An open, semantic-sharing, and collaborative-information modeling framework is needed to rapidly develop a shared data model for exchanging data among systems. openEHR is such a framework and is supported by many open software packages that help to promote information sharing and interoperability. Objective This study aims to develop a shared data model based on the openEHR modeling approach to improve the interoperability among systems for the diagnosis and treatment of COVID-19. Methods The latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. First, the guideline was analyzed and the data items used for diagnosis and treatment, and management were extracted. Second, the data items were classified and further organized into domain concepts with a mind map. Third, searching was executed in the international openEHR Clinical Knowledge Manager (CKM) to find the existing archetypes that could represent the concepts. New archetypes were developed for those concepts that could not be found. Fourth, these archetypes were further organized into a template using Ocean Template Editor. Fifth, a test case of data exchanging between the clinical data repository and clinical decision support system based on the template was conducted to verify the feasibility of the study. Results A total of 203 data items were extracted from the guideline in China, and 16 domain concepts (16 leaf nodes in the mind map) were organized. There were 22 archetypes used to develop the template for all data items extracted from the guideline. All of them could be found in the CKM and reused directly. The archetypes and templates were reviewed and finally released in a public project within the CKM. The test case showed that the template can facilitate the data exchange and meet the requirements of decision support. Conclusions This study has developed the openEHR template for COVID-19 based on the latest guideline from China using openEHR modeling methodology. It represented the capability of the methodology for rapidly modeling and sharing knowledge through reusing the existing archetypes, which is especially useful in a new and fast-changing area such as with COVID-19.

scholarly journals Development of an openEHR Template for COVID-19 Based on Clinical Guidelines (Preprint)

2020 ◽  
Author(s):  
Mengyang Li ◽  
Heather Leslie ◽  
Bin Qi ◽  
Shan Nan ◽  
Hongshuo Feng ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Model ◽  
Data Exchange ◽  
Disease Diagnosis ◽  
Information Modeling ◽  
Diagnosis And Treatment ◽  
Data Repository ◽  
Test Case ◽  
Modeling Framework ◽  
Shared Data

BACKGROUND The coronavirus disease (COVID-19) was discovered in China in December 2019. It has developed into a threatening international public health emergency. With the exception of China, the number of cases continues to increase worldwide. A number of studies about disease diagnosis and treatment have been carried out, and many clinically proven effective results have been achieved. Although information technology can improve the transferring of such knowledge to clinical practice rapidly, data interoperability is still a challenge due to the heterogeneous nature of hospital information systems. This issue becomes even more serious if the knowledge for diagnosis and treatment is updated rapidly as is the case for COVID-19. An open, semantic-sharing, and collaborative-information modeling framework is needed to rapidly develop a shared data model for exchanging data among systems. openEHR is such a framework and is supported by many open software packages that help to promote information sharing and interoperability. OBJECTIVE This study aims to develop a shared data model based on the openEHR modeling approach to improve the interoperability among systems for the diagnosis and treatment of COVID-19. METHODS The latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. First, the guideline was analyzed and the data items used for diagnosis and treatment, and management were extracted. Second, the data items were classified and further organized into domain concepts with a mind map. Third, searching was executed in the international openEHR Clinical Knowledge Manager (CKM) to find the existing archetypes that could represent the concepts. New archetypes were developed for those concepts that could not be found. Fourth, these archetypes were further organized into a template using Ocean Template Editor. Fifth, a test case of data exchanging between the clinical data repository and clinical decision support system based on the template was conducted to verify the feasibility of the study. RESULTS A total of 203 data items were extracted from the guideline in China, and 16 domain concepts (16 leaf nodes in the mind map) were organized. There were 22 archetypes used to develop the template for all data items extracted from the guideline. All of them could be found in the CKM and reused directly. The archetypes and templates were reviewed and finally released in a public project within the CKM. The test case showed that the template can facilitate the data exchange and meet the requirements of decision support. CONCLUSIONS This study has developed the openEHR template for COVID-19 based on the latest guideline from China using openEHR modeling methodology. It represented the capability of the methodology for rapidly modeling and sharing knowledge through reusing the existing archetypes, which is especially useful in a new and fast-changing area such as with COVID-19.

scholarly journals The development of a decision support information-modeling system for safety in the chemical industry

2019 ◽  
Vol 14 (4) ◽  
pp. 59-68
Author(s):  
V. V. Bannikov ◽  
T. V. Savitskaya
Keyword(s):  
Decision Support ◽  
Chemical Industry ◽  
Information Modeling ◽  
Test Case ◽  
Normative Documents ◽  
Total Destruction ◽  
Decision Supporting ◽  
Rule System ◽  
Server Architecture ◽  
Support Information

The analysis of the urgency of developing a decision and support information-modeling system for safety in the chemical industry is carried out. The article also covers the main elements of this information-modeling system. Key normative documents backing up the knowledge base of such an information-modeling system are listed below. The algorithm of the safety decision supporting information-modeling system is proposed. A database model of the safety decision supporting information-modeling system is elaborated below. A production rule system is set forth to manage issuing recommendations on the robust decision support information-modeling system in the chemical industry based on a methodological document. An implementation plan is laid out for the robust decision support information-modeling system in chemical industry. It is a ready-made software package based on two-level (client–server) architecture of information systems. This article also contains recommendations based on a test case of a tank equipment total destruction. Results of the computational experiments’ simulation in the TOXI+Risk software corresponding to the test selected values are available.

Síndrome da Anca de Ressalto

2018 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Bernardo Almeida
Keyword(s):  
Surgical Treatment ◽  
Hip Joint ◽  
Scientific Literature ◽  
Therapeutic Targets ◽  
Clinical History ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Snapping Hip ◽  
Predominant Symptom ◽  
Snapping Hip Syndrome

Snapping hip syndrome is a condition in which the predominant symptom is the snapping feelingaround the hip joint caused by a dynamic impingement between muscles or tendons and boneprominences. The etiology of the snapping hip types and consequently the therapeutic targets havebeen subjects of discussion and controversy along the years. A careful clinical history and physicalexamination is frequently enough for this disease diagnosis. Treatment is typically conservative,however when it is not successful surgical treatment is indicated, consisting on the snapping muscleor tendons lengthening. The authors review in this paper the current scientific literature about functionalanatomy, physiopathology, symptoms, diagnosis and treatment of snapping hip.

scholarly journals Availability and Use of Mobile Health Technology for Disease Diagnosis and Treatment Support by Health Workers in the Ashanti Region of Ghana: A Cross-Sectional Survey

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1233
Author(s):  
Ernest Osei ◽  
Kwasi Agyei ◽  
Boikhutso Tlou ◽  
Tivani P. Mashamba-Thompson
Keyword(s):  
Healthcare Professionals ◽  
Healthcare Delivery ◽  
Disease Diagnosis ◽  
Text Messages ◽  
Diagnosis And Treatment ◽  
Cross Sectional Survey ◽  
Mobile Wireless ◽  
Ashanti Region ◽  
Phone Calls ◽  
Treatment Support

Mobile health (mHealth) technologies have been identified as promising strategies for improving access to healthcare delivery and patient outcomes. However, the extent of availability and use of mHealth among healthcare professionals in Ghana is not known. The study’s main objective was to examine the availability and use of mHealth for disease diagnosis and treatment support by healthcare professionals in the Ashanti Region of Ghana. A cross-sectional survey was carried out among 285 healthcare professionals across 100 primary healthcare clinics in the Ashanti Region with an adopted survey tool. We obtained data on the participants’ background, available health infrastructure, healthcare workforce competency, ownership of a mobile wireless device, usefulness of mHealth, ease of use of mHealth, user satisfaction, and behavioural intention to use mHealth. Descriptive statistics were conducted to characterise healthcare professionals’ demographics and clinical features. Multivariate logistic regression analysis was performed to explore the influence of the demographic factors on the availability and use of mHealth for disease diagnosis and treatment support. STATA version 15 was used to complete all the statistical analyses. Out of the 285 healthcare professionals, 64.91% indicated that mHealth is available to them, while 35.08% have no access to mHealth. Of the 185 healthcare professionals who have access to mHealth, 98.4% are currently using mHealth to support healthcare delivery. Logistic regression model analysis significantly (p < 0.05) identified that factors such as the availability of mobile wireless devices, phone calls, text messages, and mobile apps are associated with HIV, TB, medication adherence, clinic appointments, and others. There is a significant association between the availability of mobile wireless devices, text messages, phone calls, mobile apps, and their use for disease diagnosis and treatment compliance from the chi-square test analysis. The findings demonstrate a low level of mHealth use for disease diagnosis and treatment support by healthcare professionals at rural clinics. We encourage policymakers to promote the implementation of mHealth in rural clinics.

A benefits realization management building information modeling framework for asset owners

2014 ◽  
Vol 37 ◽  
pp. 1-10 ◽  
Author(s):  
Peter E.D. Love ◽  
Jane Matthews ◽  
Ian Simpson ◽  
Andrew Hill ◽  
Oluwole A. Olatunji
Keyword(s):  
Building Information Modeling ◽  
Information Modeling ◽  
Modeling Framework ◽  
Building Information ◽  
Benefits Realization

scholarly journals Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 2039
Author(s):  
Juan F. Dols ◽  
Jaime Molina ◽  
F. Javier Camacho-Torregrosa ◽  
David Llopis-Castelló ◽  
Alfredo García
Keyword(s):  
Road Safety ◽  
Data Exchange ◽  
Building Information Modeling ◽  
Cost Savings ◽  
Safety Analysis ◽  
Information Modeling ◽  
Driving Simulators ◽  
The Road ◽  
Road Design ◽  
Building Information

The analysis of road safety is critical in road design. Complying to guidelines is not enough to ensure the highest safety levels, so many of them encourage designers to virtually recreate and test their roads, benefitting from the evolution of driving simulators in recent years. However, an accurate recreation of the road and its environment represents a real bottleneck in the process. A very important limitation lies in the diversity of input data, from different sources and requiring specific adaptations for every single simulator. This paper aims at showing a framework for recreating faster virtual scenarios by using an Industry Foundation Classes (IFC)-based file. This methodology was compared to two other conventional methods for developing driving scenarios. The main outcome of this study has demonstrated that with a data exchange file in IFC format, virtual scenarios can be faster designed to carry out safety audits with driving simulators. As a result, the editing, programming, and processing times were substantially reduced using the proposed IFC exchange file format through a BIM (Building Information Modeling) model. This methodology facilitates cost-savings, execution, and optimization resources in road safety analysis.

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